Brand impersonation: TikTok

type.inbound
and (
  // TikTok Brand Detection 
  (
    // display name contains tiktok
    strings.ilike(strings.replace_confusables(sender.display_name), '*tiktok*')
    // levenshtein distance similar to tiktok
    or strings.ilevenshtein(strings.replace_confusables(sender.display_name),
                            'tiktok'
    ) <= 1
    or (
      length(ml.logo_detect(file.message_screenshot()).brands) == 1
      and any(ml.logo_detect(file.message_screenshot()).brands,
              .name == "TikTok" and .confidence == "high"
      )
    )
    // hyphenated sender domain contains tiktok
    or strings.iends_with(sender.email.domain.root_domain, "-tiktok.com")
  )
  // OR TikTok verification language
  or (
    strings.icontains(body.current_thread.text, "tiktok")
    and (
      strings.icontains(body.current_thread.text, "verified badge")
      or strings.icontains(body.current_thread.text, "verification criteria")
      or strings.icontains(body.current_thread.text, "activate badge")
      or strings.icontains(body.current_thread.text, "verification complete")
      or strings.icontains(body.current_thread.text, "almost verified")
      or strings.icontains(body.current_thread.text, "review complete")
      or strings.icontains(body.current_thread.text, "verify profile")
    )
  )
)
and (
  // ML Topic Analysis and Credential Theft Detection
  any(ml.nlu_classifier(body.current_thread.text).topics,
      .name in (
        "Security and Authentication",
        "Secure Message",
        "Reminders and Notifications"
      )
      and .confidence in ("medium", "high")
  )
  or any(ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).topics,
         .name in (
           "Security and Authentication",
           "Secure Message",
           "Reminders and Notifications"
         )
         and .confidence in ("medium", "high")
         and beta.ocr(file.message_screenshot()).text != ""
  )
  or any(ml.nlu_classifier(body.current_thread.text).intents,
         .name == "cred_theft" and .confidence == "high"
  )
  or any(ml.nlu_classifier(beta.ocr(file.message_screenshot()).text).intents,
         .name == "cred_theft" and .confidence == "high"
  )
)
// Not from legitimate TikTok or Google domains with DMARC pass
and not (
  sender.email.domain.root_domain in $org_domains
  or (
    sender.email.domain.root_domain in (
      "tiktok.com",
      "tiktokglobalshop.com",
      "tiktokusds.com",
      "bytedance.com",
      "tiktokacademy.com",
      "webassessor.com" // used for this https://ads.tiktok.com/business/en-US/academy/tiktok-certification
    )
    and headers.auth_summary.dmarc.pass
  )
)
// negate iCloud Private Message Relay
and not (
  sender.email.domain.domain == "privaterelay.appleid.com"
  or any(headers.hops, any(.fields, .name == "X-ICLOUD-HME"))
)
// negate highly trusted sender domains unless they fail DMARC authentication
and (
  (
    sender.email.domain.root_domain in $high_trust_sender_root_domains
    and not headers.auth_summary.dmarc.pass
  )
  or sender.email.domain.root_domain not in $high_trust_sender_root_domains
)
and not profile.by_sender().solicited